# percentage of missing values ion training set
#EDA Perform debt analysis. You may take the following steps: a) Explore the top 2,500 locations where the percentage of households with a second mortgage is the highest and percent ownership is above 10 percent. Visualize using geo-map. You may keep the upper limit for the percent of households with a second mortgage to 50 percent
#Create Box and whisker plot and analyze the distribution for 2nd mortgage, home equity, good debt, and bad debt for different cities

Create a collated income distribution chart for family income, house hold income, and remaining income

Perform EDA and come out with insights into population density and age. You may have to derive new fields (make sure to weight averages for accurate measurements):

a) Use pop and ALand variables to create a new field called population density

Use male_age_median, female_age_median, male_pop, and female_pop to create a new field called median age c) Visualize the findings using appropriate chart type

Create bins for population into a new variable by selecting appropriate class interval so that the number of categories don’t exceed 5 for the ease of analysis

Analyze the married, separated, and divorced population for these population brackets

1 Very high population group has more married people and less percantage of separated and divorced couples

2 In very low population groups, there are more divorced people

Perform correlation analysis for all the relevant variables by creating a heatmap. Describe your findings.

  1. The economic multivariate data has a significant number of measured variables. The goal is to find where the measured variables depend on a number of smaller unobserved common factors or latent variables. 2. Each variable is assumed to be dependent upon a linear combination of the common factors, and the coefficients are known as loadings. Each measured variable also includes a component due to independent random variability, known as “specific variance” because it is specific to one variable. Obtain the common factors and then plot the loadings. Use factor analysis to find latent variables in our dataset and gain insight into the linear relationships in the data. Following are the list of latent variables:

Data Modeling : Linear Regression

Build a linear Regression model to predict the total monthly expenditure for home mortgages loan. Please refer ‘deplotment_RE.xlsx’. Column hc_mortgage_mean is predicted variable. This is the mean monthly mortgage and owner costs of specified geographical location. Note: Exclude loans from prediction model which have NaN (Not a Number) values for hc_mortgage_mean.

(array([3.000e+00, 1.000e+01, 6.026e+03, 5.377e+03, 2.650e+02, 1.100e+01, 4.000e+00, 9.000e+00, 2.000e+00, 2.000e+00]), array([-3744.3506714 , -2517.34353672, -1290.33640205, -63.32926738, 1163.6778673 , 2390.68500197, 3617.69213665, 4844.69927132, 6071.70640599, 7298.71354067, 8525.72067534]),